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Neural Data Science

Neural Data Science
  • Author : Erik Lee Nylen
  • Publsiher : Academic Press
  • Release : 01 February 2017
  • ISBN : 9780128040430
  • Pages : 250 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKNeural Data Science

Summary:
MATLAB remains the dominant language for scientific computing and analysis in neuroscience, but a more general purpose option - Python - is emerging. This book addresses the snake in the room by providing a beginner s introduction to the principles of computation and data analysis in neuroscience using both Python and MATLAB, which allows to transcend platform tribalism and enables coding versatility. Utilizing a Rosetta stone approach, including both MATLAB and Python in parallel Introducing the canonical data analysis cascade, standardizing the data analysis flow Introducing Comment pointers, which improve the readability of code Introducing data marning Introducing levels of coding principles, which improves the organize of code strategically, tactically and algorithmically Implementing a deeply philosophical framework of 0 to 1 teaching "


Neural Data Science

Neural Data Science
  • Author : Erik Lee Nylen,Pascal Wallisch
  • Publisher : Academic Press
  • Release : 01 February 2017
GET THIS BOOKNeural Data Science

MATLAB remains the dominant language for scientific computing and analysis in neuroscience, but a more general purpose option - Python - is emerging. This book addresses the snake in the room by providing a beginner s introduction to the principles of computation and data analysis in neuroscience using both Python and MATLAB, which allows to transcend platform tribalism and enables coding versatility. Utilizing a Rosetta stone approach, including both MATLAB and Python in parallel Introducing the canonical data analysis cascade,

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Neural Data Science

Neural Data Science
  • Author : Erik Lee Nylen,Pascal Wallisch
  • Publisher : Academic Press
  • Release : 24 February 2017
GET THIS BOOKNeural Data Science

A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. Includes discussions of both

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Analysis of Neural Data

Analysis of Neural Data
  • Author : Robert E. Kass,Uri T. Eden,Emery N. Brown
  • Publisher : Springer
  • Release : 08 July 2014
GET THIS BOOKAnalysis of Neural Data

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among

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Python Data Science Handbook

Python Data Science Handbook
  • Author : Jake VanderPlas
  • Publisher : "O'Reilly Media, Inc."
  • Release : 21 November 2016
GET THIS BOOKPython Data Science Handbook

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and

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MATLAB for Neuroscientists

MATLAB for Neuroscientists
  • Author : Pascal Wallisch,Michael E. Lusignan,Marc D. Benayoun,Tanya I. Baker,Adam Seth Dickey,Nicholas G. Hatsopoulos
  • Publisher : Academic Press
  • Release : 09 January 2014
GET THIS BOOKMATLAB for Neuroscientists

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional

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Advances in Statistical Machine Learning Methods for Neural Data Science

Advances in Statistical Machine Learning Methods for Neural Data Science
  • Author : Ding Zhou
  • Publisher : Anonim
  • Release : 26 October 2021
GET THIS BOOKAdvances in Statistical Machine Learning Methods for Neural Data Science

Innovations in neural data recording techniques are revolutionizing neuroscience and presenting both challenges and opportunities for statistical data analysis. This dissertation discusses several recent advances in neural data signal processing, encoding, decoding, and dimension reduction. Chapter 1 introduces challenges in neural data science and common statistical methods used to address them. Chapter 2 develops a new method to detect neurons and extract signals from noisy calcium imaging data with irregular neuron shapes. Chapter 3 introduces a novel probabilistic framework for modeling deconvolved calcium

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Studies in Neural Data Science

Studies in Neural Data Science
  • Author : Antonio Canale,Daniele Durante,Lucia Paci,Bruno Scarpa
  • Publisher : Springer
  • Release : 28 December 2018
GET THIS BOOKStudies in Neural Data Science

This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25–27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing,

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Data Science from Scratch

Data Science from Scratch
  • Author : Joel Grus
  • Publisher : "O'Reilly Media, Inc."
  • Release : 14 April 2015
GET THIS BOOKData Science from Scratch

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core

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Statistics for Data Science

Statistics for Data Science
  • Author : James D. Miller
  • Publisher : Packt Publishing Ltd
  • Release : 17 November 2017
GET THIS BOOKStatistics for Data Science

Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who

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Statistical Signal Processing for Neuroscience and Neurotechnology

Statistical Signal Processing for Neuroscience and Neurotechnology
  • Author : Karim G. Oweiss
  • Publisher : Academic Press
  • Release : 22 September 2010
GET THIS BOOKStatistical Signal Processing for Neuroscience and Neurotechnology

This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems. Written by experts in the field, the book is an ideal

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Data Science and Data Analytics

Data Science and Data Analytics
  • Author : Amit Kumar Tyagi
  • Publisher : CRC Press
  • Release : 22 September 2021
GET THIS BOOKData Science and Data Analytics

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms

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Deep Learning for Data Analytics

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 29 May 2020
GET THIS BOOKDeep Learning for Data Analytics

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear

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